Download full text
(177.1Kb)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://doi.org/10.48541/dcr.v12.21
Exports for your reference manager
Machines do not decide hate speech: Machine learning, power, and the intersectional approach
[collection article]
This document is a part of the following document:
Challenges and perspectives of hate speech research
Abstract The advent of social media has increased digital content - and, with it, hate speech. Advancements in machine learning help detect online hate speech at scale, but scale is only one part of the problem related to moderating it. Machines do not decide what comprises hate speech, which is part of a so... view more
The advent of social media has increased digital content - and, with it, hate speech. Advancements in machine learning help detect online hate speech at scale, but scale is only one part of the problem related to moderating it. Machines do not decide what comprises hate speech, which is part of a societal norm. Power relations establish such norms and, thus, determine who can say what comprises hate speech. Without considering this data-generation process, a fair automated hate speech detection system cannot be built. This chapter first examines the relationship between power, hate speech, and machine learning. Then, it examines how the intersectional lens - focusing on power dynamics between and within social groups - helps identify bias in the data sets used to build automated hate speech detection systems.... view less
Keywords
intersectionality; power; social media; online media; language usage; algorithm
Classification
Media Contents, Content Analysis
Natural Science and Engineering, Applied Sciences
Free Keywords
hate speech; machine learning; bias
Collection Title
Challenges and perspectives of hate speech research
Editor
Strippel, Christian; Paasch-Colberg, Sünje; Emmer, Martin; Trebbe, Joachim
Document language
English
Publication Year
2023
City
Berlin
Page/Pages
p. 355-369
Series
Digital Communication Research, 12
ISSN
2198-7610
ISBN
978-3-945681-12-1
Status
Primary Publication; peer reviewed